Faster Big Data

for Hadoop

The Real Time Hadoop Challenge

In today’s increasingly connected world, Hadoop can no longer meet big data challenges by itself. The explosion of user-generated data is being followed by an explosion of machine-generated data. While Hadoop is great for storing and processing large volumes of this data, it is not equipped to ingest it at this velocity. This has lead to the birth of a new generation of systems engineered specifically for real-time analytics. However, a modern big data solution must meet these new real-time analytical requirements while continuing to meet existing operational requirements.

A Faster Big Data Solution

Hadoop and Couchbase Server form the inner core of a faster big data solution. Hadoop enables big data analysis. It powers analytical applications. Couchbase Server enables high performance data access. It powers operational applications. Sqoop can be used to replicate data between Hadoop and Couchbase Server. It can create a big data refinery. The outer core of a faster big data solution is a stream processing system such as Storm. It enables real time analysis of data before it enters the core. Couchbase Server fills the gaps between Storm and Hadoop while powering operational applications.